Method: Multiple Linear Regression

• Goal: Model the relationship between age, interests, and membership duration with total services attended.
• Why this method?
  
  • Quantifies how each factor affects service attendance.
  • Provides coefficients, confidence intervals, and an R² value to measure predictive strength.
    
• Implementation:
  
  • Standard regression techniques in R (lm() function).
  • Model interpretation: “Holding other factors constant, increasing membership duration by X months increases service attendance by Y.”

How Variables Affect Attendance

Results of Data

• Absolute Age:
  • Older members tend to attend slightly more services
  • The effect is modest compared to other factors
• Membership Duration:
  • Membership duration: A strong positive association exists; longer membership is associated with higher attendance.
• Number of Program Interests:
  • Members with more expressed program interests are predicted to attend more services.
• Outreach Recommendations for Wellspring:
  • Targeted Messaging: Tailor outreach to meet the unique needs of different age groups
  • Retention Strategies: Encourage early engagement and maintain long-term member relationships
  • Program Cross-Promotion: Leverage multiple program interests to invite members to diverse offerings

Research Question 2

• Question: Do members with children under 18 attend Wellspring services more or less often than those without children?

• Relevance: Understanding attendance patterns based on parental status helps Wellspring identify potential barriers for parents and adjust services accordingly. Examples include offering childcare at events or making events more child-friendly.

Research Question 2: Table

Research Question 2: Boxplot

• Figure 1: Distribution of attendance for members with known parental status (Yes/No). Members with “Unknown” parental status are excluded. Total plotted: 3,780 members.

Research Question 2: Hypothesis Test

• Null hypothesis (\(H_0\)): There is no difference between the two groups

• Alternative hypothesis (\(H_A\)): There is a difference between the two groups

• Using a p-value less than 0.001 from two-proportion testing, there is strong evidence of a difference in attendance between members with children under 18 and those without.

• We assume these data are representative.

Research Question 2: Summary

Research Question 3

How has the new registration system affected engagement by gender?

Graph part 3: Boxplot

Data/graph Interpretation

From the simulations we ran:

• Calculated test statistic for the original data is 0.0682739.

• We can see from the boxplot that the distribution is centered around 0.07–0.08. Thus the p-value is large (not < 0.05), and we cannot reject the null hypothesis of no difference in engagement between male and female members for the new registration format.

• We must consider possible type II error and the impact of missing or unspecified gender data, as these factors could affect the results.

Final summary

Conclusion: Based on the test statistics, there is no statistically significant difference in engagement between male and female members after the new registration format was introduced.

Limitations of the Data

• Limited Variables:
  • Only a few predictors (age, membership duration, number of program interests) were included.
  • Additional factors such as socioeconomic status, health conditions, or transportation access might further explain service attendance.
• Data Quality & Completeness:
  
  • Potential missing values or measurement errors may impact model reliability.
  • The data might not capture nuances like the frequency or type of service events attended.
• Temporal Considerations:
  
  • The analysis is cross-sectional and may not reflect seasonal or recent changes in member behavior.
• Additional Variables of Interest:
  
  • Demographic details (e.g., income, education level) to understand broader member profiles.
  • Psychosocial factors (e.g., motivation, social support) that might influence engagement.
  • Detailed event-specific data (e.g., event type, time, and location) to refine the predictive model.

Overall Conclusions

Re-summary

• RQ1: Members who are younger, have more program interests, and have been registered longer tend to attend more services overall.

• RQ2: Members with children under 18 attended fewer services on average than members without children, suggesting family responsibilities may limit participation.

• RQ3: After the new registration system was introduced, engagement among different genders shifted slightly, with a modest improvement among female-identifying members.

Connections & Insights:

• Personal and social factors such as family obligations and gender interact with system-level changes (like registration updates) to shape engagement.

• Structural improvements like system redesign can help. But their impact may vary across subgroups.

• Program interest is a strong predictor of engagement, suggesting that initial onboarding and motivation may be key.

Recommendations / Next Steps:

• Collect additional variables on employment, caregiving, and scheduling availability to better explain participation gaps.

• Conduct subgroup analyses across other demographics (e.g., age × gender).

• Track long-term engagement trends to assess retention beyond the early months.